npj Computational Materials最新文献_第4页

Koopmans-compliant density functional framework for polaron self-trapping in titanate oxides 钛酸盐氧化物中极化子自俘获的koopmann -compliant密度泛函框架

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-07 DOI: 10.1038/s41524-026-02060-7

Soungmin Bae, Yu Kumagai

{"title":"Koopmans-compliant density functional framework for polaron self-trapping in titanate oxides","authors":"Soungmin Bae, Yu Kumagai","doi":"10.1038/s41524-026-02060-7","DOIUrl":"https://doi.org/10.1038/s41524-026-02060-7","url":null,"abstract":"Polaron self-trapping in titanate oxides—TiO₂ (rutile and anatase) and perovskites (BaTiO₃, SrTiO₃, CaTiO₃)—is crucial for their ferroelectric and photocatalytic behavior. Here, we adopt two Koopmans-compliant approaches to investigate polaron self-trapping: (1) determining Hubbard-U parameters that fulfill the generalized Koopmans’ condition for electron localization on Ti-3d and hole localization on O-2p orbitals within PBEsol (a GGA functional), SCAN, and LAK (meta-GGAs); and (2) adjusting the mixing (α) and screening (μ) parameters of the HSE06 hybrid functional to satisfy the same conditions for electron and hole polarons. Our results show that applying these self-interaction corrections (SICs) yields polaron formation energies that are consistent across different Koopmans-compliant functionals. In BaTiO₃, while applying a Hubbard-U correction to Ti-3d and O-2p spuriously stabilizes the cubic phase over the rhombohedral one, an on-site SIC correction for the electron- and hole-polaron states restores the correct rhombohedral phase stability. This work systematically compares different Koopmans-compliant approaches for modeling small-polaron trapping in functional oxides and highlights the importance of enforcing the generalized Koopmans condition for obtaining consistent polaron energetics.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"10 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147631165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data mining and computational screening of Rashba-Dresselhaus splitting and optoelectronic properties in two-dimensional perovskite materials 二维钙钛矿材料Rashba-Dresselhaus分裂和光电子特性的数据挖掘和计算筛选

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-04 DOI: 10.1038/s41524-026-02049-2

Robert Stanton, Wanyi Nie, Sergei Tretiak, Dhara J. Trivedi

{"title":"Data mining and computational screening of Rashba-Dresselhaus splitting and optoelectronic properties in two-dimensional perovskite materials","authors":"Robert Stanton, Wanyi Nie, Sergei Tretiak, Dhara J. Trivedi","doi":"10.1038/s41524-026-02049-2","DOIUrl":"https://doi.org/10.1038/s41524-026-02049-2","url":null,"abstract":"Recent developments highlighting the promise of two-dimensional perovskites have vastly increased the compositional search space in the perovskite family. This presents a great opportunity for the realization of highly performant devices and practical challenges associated with the identification of candidate materials. High-fidelity computational screening offers great value in this regard. In this study, we carry out a multiscale computational workflow, generating a dataset of two-dimensional perovskites in the Dion-Jacobson and Ruddlesden-Popper phases. Our dataset comprises ten B-site cations, four halogens, and over 20 organic cations across over 2000 materials. We compute electronic properties, thermoelectric performance, and numerous geometric characteristics. Furthermore, we introduce a framework for the high-throughput computation of Rashba-Dresselhaus splitting. Finally, we use this dataset to train machine learning models for the accurate prediction of band gaps, candidate Rashba-Dresselhaus materials, and partial charges. The work presented herein can aid future investigations of two-dimensional perovskites with targeted applications in mind.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"86 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147620182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DFT and MLIP study of solute segregation to coherent and semi-coherent α-Fe/Fe3C interfaces α-Fe/Fe3C界面溶质偏析的DFT和MLIP研究

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-03 DOI: 10.1038/s41524-026-02061-6

Amin Reiners-Sakic, Ronald Schnitzer, David Holec

{"title":"DFT and MLIP study of solute segregation to coherent and semi-coherent α-Fe/Fe3C interfaces","authors":"Amin Reiners-Sakic, Ronald Schnitzer, David Holec","doi":"10.1038/s41524-026-02061-6","DOIUrl":"https://doi.org/10.1038/s41524-026-02061-6","url":null,"abstract":"Solute segregation to interfaces strongly affects material behavior. Most theoretical studies focus on grain boundaries and coherent interfaces, whereas semi-coherent interfaces remain largely unexplored due to structural complexity exceeding the practical capability of density functional theory (DFT) or chemical complexity constrained by the availability of classical interatomic potentials. Here, we investigate solute segregation to the coherent and semi-coherent α -Fe/Fe 3 C interface and its mechanical impact using novel universal machine-learning interatomic potentials (uMLIPs). DFT calculated solution enthalpies, segregation energetics, and cohesion changes at the coherent interface serve to benchmark several state-of-the-art uMLIPs, identifying GRACE-2L-OAM and GRACE-2L-OMAT as most consistent with quantum-mechanical predictions. Among the studied tramp and trace elements, Cu shows the strongest segregation energy to the coherent interface of ≈ − 0.3 eV. However, at the semi-coherent interface, all elements exhibit significantly stronger segregation energetics, reaching below ≈ − 1.5 eV, with the deepest traps near the misfit dislocation core. Sb, Sn, P, and As strongly reduce the coherent interface cohesion, Cu mildly, while Ni has a negligible influence, and Cr and Mo slightly enhance cohesion. At the semi-coherent interface, all solutes except P promote embrittlement, with Sn and Sb showing the strongest effect. These results underscore the importance of realistic interface structures for predictive materials design.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"114 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147611925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A natural language processing to causality framework for robust knowledge extraction of CO₂ hydrogenation with batch effect control 基于批效应控制的CO₂加氢鲁棒知识提取的自然语言处理到因果关系框架

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-03 DOI: 10.1038/s41524-026-02065-2

WeiHang Xu, LiHua Bai, Ji Qi, XiaoYing Sun, BaiRan Wang, Zhen Zhao, Bo Li

引用次数: 0

Structure divide-and-conquer: dual graph representation for accurate ionic transport barrier prediction of inorganic compounds 结构分而治之：精确预测无机化合物离子输运势垒的双图表示

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-03 DOI: 10.1038/s41524-026-02058-1

Zhengwei Yang, Linhan Wu, Bing He, Maxim Avdeev, Siqi Shi, Yue Liu

引用次数: 0

Physics-informed GCN-LSTM framework for long-term forecasting of 2D and 3D microstructure evolution 基于物理信息的GCN-LSTM框架用于二维和三维微观结构演变的长期预测

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-03 DOI: 10.1038/s41524-026-01999-x

Hamidreza Razavi, Nele Moelans

引用次数: 0

Angular relational knowledge distillation of machine learning interatomic potentials for scalable catalyst exploration 面向可扩展催化剂探索的机器学习原子间势的角关系知识蒸馏

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-03 DOI: 10.1038/s41524-026-02062-5

Hyukjun Lim, Seokhyun Choung, Jinuk Moon, Jeong Woo Han

引用次数: 0

Photoinduced ultrafast polarization reversal and terahertz emission in an organic ferroelectric crystal 有机铁电晶体的光致超快极化反转和太赫兹发射

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-02 DOI: 10.1038/s41524-026-02066-1

Bozhu Chen, Qunfang Gu, Huanhuan Yang, Jianguo Si, Miao Liu, Jiyu Xu, Sheng Meng

{"title":"Photoinduced ultrafast polarization reversal and terahertz emission in an organic ferroelectric crystal","authors":"Bozhu Chen, Qunfang Gu, Huanhuan Yang, Jianguo Si, Miao Liu, Jiyu Xu, Sheng Meng","doi":"10.1038/s41524-026-02066-1","DOIUrl":"https://doi.org/10.1038/s41524-026-02066-1","url":null,"abstract":"Croconic acid represents a prototype organic dielectric, whose polarization exhibits a rapid response to external stimulus owing to its proton-transfer type ferroelectricity. Photoexcitation has shown a great potential to achieve efficient and fast polarization reversal of croconic acid crystal, but the microscopic dynamics of photoinduced ultrafast polarization reversal remain elusive due to the delicate interactions and ultrafast timescale. Here, we unravel the microscopic mechanism of photoinduced complete polarization reversal in croconic acid crystal via first-principles coupled electron-nuclear dynamics simulations. Upon photoexcitation, the croconic acid crystal undergoes the ultrafast depolarization within 150 femtoseconds. The depolarization is primarily governed by proton dynamics, with electronic contribution following those of protons. The depolarization and proton transfer take place in a half-transfer mode, driven by the different charge redistribution of two groups of hydrogen atoms. Ab-initio molecular dynamics simulations confirm the complete reversal of polarization and transfer of all protons during the picosecond relaxation process. Moreover, the sudden polarization switch and subsequent oscillations lead to efficient terahertz emission, consistent well with experiments.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"2 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147611852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Benchmarking universal machine learning interatomic potentials for supported nanoparticles: decoupling energy accuracy from structural exploration 支持纳米粒子的通用机器学习原子间势的基准：从结构探索中解耦能量精度

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-02 DOI: 10.1038/s41524-026-02043-8

Jiayan Xu, Abhirup Patra, Amar Deep Pathak, Sharan Shetty, Detlef Hohl, Roberto Car

{"title":"Benchmarking universal machine learning interatomic potentials for supported nanoparticles: decoupling energy accuracy from structural exploration","authors":"Jiayan Xu, Abhirup Patra, Amar Deep Pathak, Sharan Shetty, Detlef Hohl, Roberto Car","doi":"10.1038/s41524-026-02043-8","DOIUrl":"https://doi.org/10.1038/s41524-026-02043-8","url":null,"abstract":"Supported nanoparticle catalysts are widely used in the chemical industry. Computational modeling of supported nanoparticles based on density functional theory (DFT) often involves structural searches of stable local minimum energy configurations and molecular dynamics simulations at finite temperature. These are computationally demanding tasks that are intractable within DFT for large systems. In the last two decades, machine learning interatomic potentials (MLIPs) have been successfully used to substantially increase the size and time scales accessible to simulations approximating DFT accuracy. However, training reliable MLIPs is non-trivial as it requires many costly DFT calculations. Recently, several universal MLIPs (uMLIPs) have been developed, which are trained on large datasets that cover a wide range of molecules and materials. Here, we benchmark the accuracy and the efficiency of these uMLIPs in describing Cu nanoparticles supported on Al2O3 surfaces against our domain-specific DP-UniAlCu model. We find that the MACE-OMAT can reproduce reasonably well the low-energy structures found in global optimization at an energy accuracy comparable to DP-UniAlCu. Interestingly, the MatterSim-v1.0.0-1M model, which exhibits larger deviations in the binding energies, can find even more stable configurations than the other two models in some supported nanoparticle sizes, showing its capability in structure exploration. For MD simulations, MACE-OMAT and MatterSim-v1.0.0-1M can qualitatively reproduce the mean-squared displacements of Cu atoms (MSDCu) predicted by DP-UniAlCu, albeit at roughly two orders of magnitude higher cost. We demonstrate that the uMLIPs can be very useful in simulating supported nanoparticles even without any fine-tuning, though their reduced efficiency remains a limiting factor for large-scale simulations.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"5 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147586030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Accelerating discovery of infrared nonlinear optical materials with large shift current via high-throughput screening 通过高通量筛选加速发现具有大位移电流的红外非线性光学材料

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-01 DOI: 10.1038/s41524-026-02064-3

Aiqin Yang, Dian Jin, Mingkang Liu, Daye Zheng, Qi Wang, Qiangqiang Gu, Jian-Hua Jiang

{"title":"Accelerating discovery of infrared nonlinear optical materials with large shift current via high-throughput screening","authors":"Aiqin Yang, Dian Jin, Mingkang Liu, Daye Zheng, Qi Wang, Qiangqiang Gu, Jian-Hua Jiang","doi":"10.1038/s41524-026-02064-3","DOIUrl":"https://doi.org/10.1038/s41524-026-02064-3","url":null,"abstract":"Discovering nonlinear optical (NLO) materials with strong shift current response, particularly in the infrared (IR) regime, is essential for next-generation optoelectronics yet remains highly challenging in both experiments and theory, which still largely relies on case-by-case studies. Here, we employ a high-throughput screening strategy, applying a multi-step filter to the Materials Project database (>154,000 materials), which yielded 2519 candidate materials for detailed first-principles evaluation. From these calculations, we identify 32 NLO materials with strong shift current response (σ > 100 μA/V2). Our work reveals that layered structures with C3v symmetry and heavy p-block elements (e.g., Te and Sb) exhibit apparent superiority in enhancing shift current. More importantly, 9 of these compounds show shift current response peaks in the IR region, with the strongest reaching 616 μA/V2, holding significant application potential in fields such as IR photodetection, sensing, and energy harvesting. Beyond identifying promising candidates, this work establishes a comprehensive and high-quality first-principles dataset for NLO response, providing a solid foundation for future AI-driven screening and accelerated discovery of high-performance NLO materials, as demonstrated by a prototype machine-learning application.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"52 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147586032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0